Research Papers - Department of Civil Engineering

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    Estimation of Potential Evapotranspiration across Sri Lanka Using a Distributed Dual-Source Evapotranspiration Model under Data Scarcity
    (Hindawi, 2022-04-04) Senatilleke, U; Abeysiriwardana, H. D; Makumbura, R. K; Faisal Anwar, A. H. M; Rathnayake, U
    Evapotranspiration estimations are not common in developing countries though most of them have water scarcities for agricultural purposes. erefore, it is essential to estimate the rates of evapotranspiration based on the available climatic parameters. Proper estimations of evapotranspiration are unavailable to Sri Lanka, even though the country has a signi cant agricultural contribution to its economy. erefore, the Shuttleworth–Wallace (S-W) model, a process-based two-source potential evapotranspiration (PET) model, is implemented to simulate the spatiotemporal distribution of PET, evaporation from soil (ETs), and transpiration from vegetation canopy (ETc) across the total landmass of Sri Lanka. e country was divided into a grid with 6km × 6km cells. e meteorological data, including rainfall, temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, for 14 meteorological stations were used in this analysis. ey were interpolated using Inverse Distance Weighting (IDW), Universal kriging, and iessen polygon methods as appropriate so that the generated thematic layers were fairly closer to reality. Normalized Dierence Vegetation Index (NDVI) and soil moisture data were retrieved from publicly available online domains, while the threshold values of vegetation parameters were taken from the literature. Notwithstanding many approximations and uncertainties associated with the input data, the implemented model displayed an adequate ability to capture the spatiotemporal distribution of PET and its components. A comparison between predicted PET and recorded pan evaporations resulted in a root mean square error (RMSE) of 0.75 mm/day. e model showed high sensitivity to Leaf Area Index (LAI). e model revealed that both spatial and temporal distribution of PETis highly correlated with the incoming solar radiation uxes and aected by the rainfall seasons and cultivation patterns. e model predicted PET values accounted for 80–90% and 40–60% loss of annual mean rainfall, respectively, in the drier and wetter parts of the country. e model predicted a 0.65 ratio of annual transpiration to annual evapotranspiration.
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    Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression
    (Springer, Cham, 2022-02) Abeysiriwardana, H. D; Gomes, P. I. A
    This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses. First, data for eight independent variables, i.e., soil moisture content, soil organic content, compaction of soil (soil toughness), plant root strength, crop biomass, tree diameter at knee height, Shannon Wiener Index (SWI) for trees and herbs was assembled from field tests at two historic landslide locations: Aranayaka and Kurukudegama, Sri Lanka. An economical, finer resolution database was obtained as the field tests were not cost-prohibitive. The logistic regression (LR) analysis showed that soil moisture content, compaction of soil, SWI for trees and herbs were statistically significant at P < 0.05. The variance inflation factors (VIFs) were computed to test for multicollinearity. VIF values (< 2) confirmed the absence of multicollinearity between four independent variables in the LR model. Receiver Operating Characteristics (ROC) curve and Confusion Metrix (CM) methods were used to validate the model. In ROC analysis, areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%, respectively, demonstrating the model’s excellent compatibility and predictability. According to the CM, the model demonstrated a 79.6% accuracy, 63.6% precision, 100% recall, and a F-measure of 77.8%. The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics. Finally, the susceptibility map, which was then classified as low, medium, and highly susceptible areas based on the natural breaks (Jenks) method, was generated using geographical information systems (GIS) techniques. All the historic landslide locations fell into the high susceptibility areas. Thus, validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.